# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

r"""Evaluation executable for detection models.
This executable is used to evaluate DetectionModels. There are two ways of
configuring the eval job.
1) A single pipeline_pb2.TrainEvalPipelineConfig file maybe specified instead.
In this mode, the --eval_training_data flag may be given to force the pipeline
to evaluate on training data instead.
Example usage:
    ./eval \
        --logtostderr \
        --checkpoint_dir=path/to/checkpoint_dir \
        --eval_dir=path/to/eval_dir \
        --pipeline_config_path=pipeline_config.pbtxt
2) Three configuration files may be provided: a model_pb2.DetectionModel
configuration file to define what type of DetectionModel is being evaluated, an
input_reader_pb2.InputReader file to specify what data the model is evaluating
and an eval_pb2.EvalConfig file to configure evaluation parameters.
Example usage:
    ./eval \
        --logtostderr \
        --checkpoint_dir=path/to/checkpoint_dir \
        --eval_dir=path/to/eval_dir \
        --eval_config_path=eval_config.pbtxt \
        --model_config_path=model_config.pbtxt \
        --input_config_path=eval_input_config.pbtxt
"""
import functools
import os

import tensorflow as tf
from object_detection import evaluator
from object_detection.builders import dataset_builder, model_builder
from object_detection.utils import config_util, dataset_util, label_map_util

tf.logging.set_verbosity(tf.logging.INFO)

flags = tf.app.flags
flags.DEFINE_boolean('eval_training_data', False,
                     'If training data should be evaluated for this job.')
flags.DEFINE_string('checkpoint_dir', '',
                    'Directory containing checkpoints to evaluate, typically set to `train_dir` used in the training job.')
flags.DEFINE_string('eval_dir', '',
                    'Directory to write eval summaries to.')
flags.DEFINE_string('pipeline_config_path', '',
                    'Path to a pipeline_pb2.TrainEvalPipelineConfig config file. If provided, other configs are ignored')
flags.DEFINE_string('eval_config_path', '',
                    'Path to an eval_pb2.EvalConfig config file.')
flags.DEFINE_string('input_config_path', '',
                    'Path to an input_reader_pb2.InputReader config file.')
flags.DEFINE_string('model_config_path', '',
                    'Path to a model_pb2.DetectionModel config file.')
flags.DEFINE_boolean('run_once', False,
                     'Option to only run a single pass of evaluation. '
                     'Overrides the `max_evals` parameter in the provided config.')
FLAGS = flags.FLAGS


def main(unused_argv):
    assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
    assert FLAGS.eval_dir, '`eval_dir` is missing.'
    tf.gfile.MakeDirs(FLAGS.eval_dir)
    if FLAGS.pipeline_config_path:
        configs = config_util.get_configs_from_pipeline_file(FLAGS.pipeline_config_path)
        tf.gfile.Copy(
            FLAGS.pipeline_config_path,
            os.path.join(FLAGS.eval_dir, 'pipeline.config'),
            overwrite=True
        )
    else:
        configs = config_util.get_configs_from_multiple_files(
            model_config_path=FLAGS.model_config_path,
            eval_config_path=FLAGS.eval_config_path,
            eval_input_config_path=FLAGS.input_config_path
        )
        for name, config in [('model.config', FLAGS.model_config_path),
                             ('eval.config', FLAGS.eval_config_path),
                             ('input.config', FLAGS.input_config_path)]:
            tf.gfile.Copy(config, os.path.join(FLAGS.eval_dir, name), overwrite=True)

    model_config = configs['model']
    eval_config = configs['eval_config']
    input_config = configs['eval_input_config']
    if FLAGS.eval_training_data:
        input_config = configs['train_input_config']

    model_fn = functools.partial(
        model_builder.build,
        model_config=model_config,
        is_training=False)

    def get_next(config):
        return dataset_util.make_initializable_iterator(dataset_builder.build(config)).get_next()

    create_input_dict_fn = functools.partial(get_next, input_config)

    label_map = label_map_util.load_labelmap(input_config.label_map_path)
    max_num_classes = max([item.id for item in label_map.item])
    categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes)

    if FLAGS.run_once:
        eval_config.max_evals = 1

    metrics = evaluator.evaluate(
        create_input_dict_fn,
        model_fn,
        eval_config,
        categories,
        FLAGS.checkpoint_dir,
        FLAGS.eval_dir
    )
    print(metrics)


if __name__ == '__main__':
    tf.app.run()
